The Sequence of Action Model: Leveraging the Sequence of Attempts and Hints

Intelligent Tutoring Systems (ITS) have been proven to be efficient providing student assistance and assessing their performance when they do their homework. Researchers have analyzed how students’ knowledge grows and predict their performance from within intelligent tutoring systems. Most of them focus on using correctness of the previous question or the number of hints and attempts students need to predict their future performance, but ignore the sequence of hints and attempts. In this research work, we build a Sequence of Actions (SOA) model taking advantage of the sequence of hints and attempts a student needed for the previous question to predict students’ performance. A two step modeling methodology is put forward in the work and is a combination of Tabling method and the Logistic Regression. We compared SOA with Knowledge Tracing (KT) and Assistance Model (AM) and combinations of SOA/AM and KT. The experimental results showed that the Sequence of Action model has reliably better predictive accuracy than KT and AM and its performance of prediction is improved after combining with KT.